Dask: Transfer content to new wiki and fix checks

doc.zih.tu-dresden.de/docs/software/Dask.md

+# Dask
+
+**Dask** is an open-source library for parallel computing. Dask is a flexible library for parallel
+computing in Python.
+
+Dask natively scales Python. It provides advanced parallelism for analytics, enabling performance at
+scale for some of the popular tools. For instance: Dask arrays scale Numpy workflows, Dask
+dataframes scale Pandas workflows, Dask-ML scales machine learning APIs like Scikit-Learn and
+XGBoost.
+
+Dask is composed of two parts:
+
+- Dynamic task scheduling optimized for computation and interactive
+  computational workloads.
+- Big Data collections like parallel arrays, data frames, and lists
+  that extend common interfaces like NumPy, Pandas, or Python
+  iterators to larger-than-memory or distributed environments. These
+  parallel collections run on top of dynamic task schedulers.
+
+Dask supports several user interfaces:
+
+High-Level:
+
+- Arrays: Parallel NumPy
+- Bags: Parallel lists
+- DataFrames: Parallel Pandas
+- Machine Learning : Parallel Scikit-Learn
+- Others from external projects, like XArray
+
+Low-Level:
+
+- Delayed: Parallel function evaluation
+- Futures: Real-time parallel function evaluation
+
+## Installation
+
+### Installation Using Conda
+
+Dask is installed by default in [Anaconda](https://www.anaconda.com/download/). To install/update
+Dask on a Taurus with using the [conda](https://www.anaconda.com/download/) follow the example:
+
+```Bash
+# Job submission in ml nodes with allocating: 1 node, 1 gpu per node, 4 hours
+srun -p ml -N 1 -n 1 --mem-per-cpu=5772 --gres=gpu:1 --time=04:00:00 --pty bash
+```
+
+Create a conda virtual environment. We would recommend using a workspace. See the example (use
+`--prefix` flag to specify the directory).
+
+**Note:** You could work with simple examples in your home directory (where you are loading by
+default). However, in accordance with the
+[HPC storage concept](../data_management/HPCStorageConcept2019.md) please use a
+[workspaces](../data_management/Workspaces.md) for your study and work projects.
+
+```Bash
+conda create --prefix /scratch/ws/0/aabc1234-Workproject/conda-virtual-environment/dask-test python=3.6
+```
+
+By default, conda will locate the environment in your home directory:
+
+```Bash
+conda create -n dask-test python=3.6
+```
+
+Activate the virtual environment, install Dask and verify the installation:
+
+```Bash
+ml modenv/ml
+ml PythonAnaconda/3.6
+conda activate /scratch/ws/0/aabc1234-Workproject/conda-virtual-environment/dask-test python=3.6
+which python
+which conda
+conda install dask
+python
+
+from dask.distributed import Client, progress
+client = Client(n_workers=4, threads_per_worker=1)
+client
+```
+
+### Installation Using Pip
+
+You can install everything required for most common uses of Dask (arrays, dataframes, etc)
+
+```Bash
+srun -p ml -N 1 -n 1 --mem-per-cpu=5772 --gres=gpu:1 --time=04:00:00 --pty bash
+
+cd /scratch/ws/0/aabc1234-Workproject/python-virtual-environment/dask-test
+
+ml modenv/ml
+module load PythonAnaconda/3.6
+which python
+
+python3 -m venv --system-site-packages dask-test
+source dask-test/bin/activate
+python -m pip install "dask[complete]"
+
+python
+from dask.distributed import Client, progress
+client = Client(n_workers=4, threads_per_worker=1)
+client
+```
+
+Distributed scheduler
+
+?
+
+## Run Dask on Taurus
+
+The preferred and simplest way to run Dask on HPC systems today both for new, experienced users or
+administrator is to use [dask-jobqueue](https://jobqueue.dask.org/).
+
+You can install dask-jobqueue with `pip` or `conda`
+
+Installation with Pip
+
+```Bash
+srun -p haswell -N 1 -n 1 -c 4 --mem-per-cpu=2583 --time=01:00:00 --pty bash
+cd
+/scratch/ws/0/aabc1234-Workproject/python-virtual-environment/dask-test
+ml modenv/ml module load PythonAnaconda/3.6 which python
+
+source dask-test/bin/activate pip
+install dask-jobqueue --upgrade # Install everything from last released version
+```
+
+Installation with Conda
+
+```Bash
+srun -p haswell -N 1 -n 1 -c 4 --mem-per-cpu=2583 --time=01:00:00 --pty bash
+
+ml modenv/ml module load PythonAnaconda/3.6 source
+dask-test/bin/activate
+
+conda install dask-jobqueue -c conda-forge\</verbatim>
+```

doc.zih.tu-dresden.de/mkdocs.yml

@@ -24,20 +24,22 @@ nav:
       - Singularity Recicpe Hints: software/SingularityRecipeHints.md
       - Singularity Example Definitions: software/SingularityExampleDefinitions.md
     - Custom Easy Build Modules: software/CustomEasyBuildEnvironment.md
-    - Get started with HPC-DA: software/GetStartedWithHPCDA.md
     - Mathematics: software/Mathematics.md
-    - Machine Learning: software/MachineLearning.md
-    - Deep Learning: software/DeepLearning.md
     - Visualization: software/Visualization.md
-    - Data Analytics with R: software/DataAnalyticsWithR.md
-    - Data Analytics with Python: software/Python.md
-    - Tensorflow: 
-      - Tensorflow Overview: software/TensorFlow.md
-      - Tensorflow in Container: software/TensorFlowContainerOnHPCDA.md
-      - Tensorflow in JupyterHub: software/TensorFlowOnJupyterNotebook.md 
-    - Keras: software/Keras.md
-    - Power AI: software/PowerAI.md
-    - PyTorch: software/PyTorch.md
+    - HPC-DA:
+      - Get started with HPC-DA: software/GetStartedWithHPCDA.md
+      - Machine Learning: software/MachineLearning.md
+      - Deep Learning: software/DeepLearning.md
+      - Data Analytics with R: software/DataAnalyticsWithR.md
+      - Data Analytics with Python: software/Python.md
+      - Tensorflow: 
+        - Tensorflow Overview: software/TensorFlow.md
+        - Tensorflow in Container: software/TensorFlowContainerOnHPCDA.md
+        - Tensorflow in JupyterHub: software/TensorFlowOnJupyterNotebook.md 
+      - Keras: software/Keras.md
+      - Dask: software/Dask.md
+      - Power AI: software/PowerAI.md
+      - PyTorch: software/PyTorch.md
     - Computational Fluid Dynamics (CFD): software/CFD.md
     - FAQs: software/modules-faq.md
     - Bio Informatics: software/Bioinformatics.md

twiki2md/root/HPCDA/Dask.md

-# Dask
-
-\<span style="font-size: 1em;"> **Dask** is an open-source library for
-parallel computing. Dask is a flexible library for parallel computing in
-Python.\</span>
-
-Dask natively scales Python. It\<span style="font-size: 1em;"> provides
-advanced parallelism for analytics, enabling performance at scale for
-some of the popular tools. For instance: Dask arrays scale Numpy
-workflows, Dask dataframes scale Pandas workflows, Dask-ML scales
-machine learning APIs like Scikit-Learn and XGBoost\</span>
-
-Dask is composed of two parts:
-
--   Dynamic task scheduling optimized for computation and interactive
-    computational workloads.
--   Big Data collections like parallel arrays, data frames, and lists
-    that extend common interfaces like NumPy, Pandas, or Python
-    iterators to larger-than-memory or distributed environments. These
-    parallel collections run on top of dynamic task schedulers.
-
-Dask supports several user interfaces:
-
-High-Level:
-
--   Arrays: Parallel NumPy
--   Bags: Parallel lists
--   DataFrames: Parallel Pandas
--   Machine Learning : Parallel Scikit-Learn
--   Others from external projects, like XArray
-
-Low-Level:
-
--   Delayed: Parallel function evaluation
--   Futures: Real-time parallel function evaluation
-
-## Installation
-
-### installation using Conda
-
-Dask is installed by default in
-[Anaconda](https://www.anaconda.com/download/). To install/update Dask
-on a Taurus with using the [conda](https://www.anaconda.com/download/)
-follow the example:
-
-    srun -p ml -N 1 -n 1 --mem-per-cpu=5772 --gres=gpu:1 --time=04:00:00 --pty bash      #Job submission in ml nodes with allocating: 1 node, 1 gpu per node, 4 hours
-
-Create a conda virtual environment. We would recommend using a
-workspace. See the example (use `--prefix` flag to specify the
-directory)\<br />\<span style="font-size: 1em;">Note: You could work
-with simple examples in your home directory (where you are loading by
-default). However, in accordance with the \</span>\<a
-href="HPCStorageConcept2019" target="\_blank">storage concept\</a>\<span
-style="font-size: 1em;">,\</span>** please use \<a href="WorkSpaces"
-target="\_blank">workspaces\</a> for your study and work projects.**
-
-    conda create --prefix /scratch/ws/0/aabc1234-Workproject/conda-virtual-environment/dask-test python=3.6
-
-By default, conda will locate the environment in your home directory:
-
-    conda create -n dask-test python=3.6
-
-Activate the virtual environment, install Dask and verify the
-installation:
-
-    ml modenv/ml
-    ml PythonAnaconda/3.6
-    conda activate /scratch/ws/0/aabc1234-Workproject/conda-virtual-environment/dask-test python=3.6
-    which python
-    which conda
-    conda install dask
-    python
-
-    from dask.distributed import Client, progress
-    client = Client(n_workers=4, threads_per_worker=1)
-    client
-
-### installation using Pip
-
-You can install everything required for most common uses of Dask
-(arrays, dataframes, etc)
-
-    srun -p ml -N 1 -n 1 --mem-per-cpu=5772 --gres=gpu:1 --time=04:00:00 --pty bash
-
-    cd /scratch/ws/0/aabc1234-Workproject/python-virtual-environment/dask-test
-
-    ml modenv/ml
-    module load PythonAnaconda/3.6
-    which python
-
-    python3 -m venv --system-site-packages dask-test
-    source dask-test/bin/activate
-    python -m pip install "dask[complete]"
-
-    python
-    from dask.distributed import Client, progress
-    client = Client(n_workers=4, threads_per_worker=1)
-    client
-
-Distributed scheduler
-
-?
-
-## Run Dask on Taurus
-
-\<span style="font-size: 1em;">The preferred and simplest way to run
-Dask on HPC systems today both for new, experienced users or
-administrator is to use \</span>
-[dask-jobqueue](https://jobqueue.dask.org/)\<span style="font-size:
-1em;">.\</span>
-
-You can install dask-jobqueue with `pip <span>or</span>` `conda`
-
-Installation with Pip
-
-    srun -p haswell -N 1 -n 1 -c 4 --mem-per-cpu=2583 --time=01:00:00 --pty bash
-
-\<verbatim>cd
-/scratch/ws/0/aabc1234-Workproject/python-virtual-environment/dask-test
-ml modenv/ml module load PythonAnaconda/3.6 which python
-
-source dask-test/bin/activate pip install dask-jobqueue --upgrade #
-Install everything from last released version\</verbatim>
-
-Installation with Conda
-
-    srun -p haswell -N 1 -n 1 -c 4 --mem-per-cpu=2583 --time=01:00:00 --pty bash
-
-\<verbatim>ml modenv/ml module load PythonAnaconda/3.6 source
-dask-test/bin/activate
-
-conda install dask-jobqueue -c conda-forge\</verbatim>
-
--- Main.AndreiPolitov - 2020-08-26
-
-**\<br />**